One area of automotive vehicle control where a high level of autonomy is both desirable and realizable is when travelling on one way roads. During vehicle travel on one way roads (e.g. highways) a high percentage of traffic accidents and fatalities are related to the human factor in lane change and overtake manoeuvres.
Thus, advanced driver assistance systems (ADAS) or fully automated systems for these types of manoeuvres are of great interest.
Manoeuvre generation with respect to surrounding vehicles can be viewed as an obstacle avoidance problem. Obstacle avoidance is a part of dynamic path planning since a collision free trajectory is crucial for performance.
Several methods for handling the obstacle avoidance problem have been proposed, where the most common include grid/graph based search, and relative velocity and distance based cost functions or conditions.
In grid/graph based search methods such as e.g. A*, D*, and rapidly exploring random trees (RRT), grid cells or graph nodes are assigned obstacle dependent cost, thus allowing the algorithm to find collision free trajectories. However, the algorithms can require significant computer memory and the efficiency of the algorithms is heavily dependent on the heuristics used.
Cost functions or constraints based on the distance and relative velocity to obstacles are commonly used due to their straightforwardness and simplicity. By either adding a cost term that increases when obstacles are in close proximity and the risk of collision is imminent or as a constraint on e.g. minimum distance allowed to obstacles, collision free trajectories can be achieved.
However, these types of cost terms or constraints are normally non-linear and/or non-convex, thus providing no guarantee of generating an optimal solution.
Although the above mentioned approaches for obstacle avoidance does give good results in a number of applications they also come with various drawbacks where the main drawback is the trade-off between required computational resources and solution optimality.